One of the central societal challenges is to prolong independent living for elderly and promote well. Personalized robotic rehabilitation and assistance is considered one of the enabling technologies with control design playing a significant role. Focussing on sensorimotor rehabilitation and assistance, personalized control should be able to adapt to the high inter-personal variability in human motor behavior but also to intra-personal changes over time. Control adaptation is further challenged by the sparsity of person-specific data because calibration routines need to be brief for user acceptance. Above all, guaranteed safety is one of the key requirements.
In this talk we will present recent results on learning-based control with performance and safety guarantees for highly uncertain systems with particular focus on challenges arising from personalized rehabilitation and assistance. In order to achieve high sample efficiency as well as transparency of the system, available knowledge of neuromuscular dynamic models will be exploited and and augmented by Bayesian non-parametric model components. Epistemic uncertainty due to limited training data will explicitly taken into account in the control design in order to achieve uncertainty-aware behavior of the closed loop system. Online learning as well as realtime capabilities are further important aspects. The results will be demonstrated in user intention-driven shared control designs for upper limb rehabilitation and assistance with exoskeletons and functional electrical stimulation. Furthermore, the limits of learning control and personalization will be discussed.